Medical image comparison method and system thereof

ABSTRACT

A medical image comparison method is firstly to obtain plural images of the same body at different time points. Then, obtain a first feature point group by detecting feature points in the first image captured at a first time point, and a second feature point group by identifying feature points in the second image captured at a second time point. An overlapping image information is generated by aligning the second image with the first image according to the first and second feature point groups. Then, window areas corresponding to a first matching image and a second matching image of the overlapping image information are extracted one by one by sliding a window mask, and an image difference ratio for each of the window areas is calculated. In addition, a medical image comparison system is also provided.

TECHNICAL FIELD

The present disclosure relates to a medical image comparison method andsystem thereof, and more particularly, to medical image comparisonmethod and system for assisting medical doctors to do medical diagnosis.

BACKGROUND

With rapid advance in medical image diagnosis, medical personnel isbecoming more and more accustomed to use medical imaging as an assistingmeans for diagnosing the clinical condition of a patient, and thereby,minute pathological changes in living organisms can be detected beforethe appearance of symptoms.

Retinal examination is a diagnostic procedure that allows theophthalmologist to obtain a better view of the retinal of you eye and tolook for signs of eye disease such as retinal detachment, opticneuritis, macular degeneration, glaucoma and other retinal issues. It isnoted that eye is the only organ whose nerves can be detected in anon-invasive manner, and thus generally it can be treated as a microcosmof all the important organs in our body. Therefore, retinal images notonly can be used by ophthalmologist for diagnosing eye diseases, butalso clinically it can reveal representative pathological changes oforgans other than eyes. That is, physicians of other disciplines, suchas metabolism or neurology, can use retinal images for detecting earlypathological changes of other diseases, such as diabetes, high bloodpressure, high blood cholesterol, auto immune disease, etc.

For those conventional medical imaging techniques that are currentlyavailable, such as the aforesaid fundus imaging, patients have to besubjected to an retinal examination once every two years for trackingthe morphology of a target area. However, since there may be differencesin imaging angle, the use of light source, luminance and parameterconfiguration between different retinal imaging processes, the resultedretinal images are different accordingly. Thus, physicians have tomanually search and find all the differences between retinal images thatare taken at different time, which not only it is a time-consuming andlabor-intensive task, but also the manual difference identification mayeasily leads to misdiagnosis as human error is not a easy task toprevent. Moreover, owing to the subjective valuation difference,different physicians may have different identification results about thesame retinal image.

Therefore, it is in need of an improved medical image comparison methodand system thereof, capable of overcoming the aforesaid problems.

SUMMARY

The present disclosure provides a medical image comparison method andsystem, which can be used for allowing a physician to compare medicalimages of a specific area in a patient that are taken at different timeso as to locate a region of variation from the medical images, and thusfor assisting the physicians to do medical diagnosis.

In an embodiment, the present disclosure provides a medical imagecomparison method, which comprises the steps of: obtaining a pluralityof images of a body at different time points, while allowing the pluralimages to include a first image captured at a first time point and asecond image captured at a second time point; obtaining a first featurepoint group by detecting feature points in the first image, whileobtaining a second feature point group by detecting feature points inthe second image; enabling an overlapping image information to begenerated by aligning the second image with the first image according tothe first feature point group and the second feature point group, whileallowing the overlapping image information to include a first matchingimage corresponding to the first image and a second matching imagecorresponding to the second image; and sequentially extractingcorresponding window areas from the first matching image and the secondmatching image in the overlapping image information respectively by theuse of a sliding window mask, while calculating an image differenceratio for each of the window areas according to the ratio between thenumber of matching points and the number of unmatched points in thecorresponding window areas of the first and the second matching images.

In an embodiment, the present disclosure provides a medical imagecomparison system, which comprises: an image processing device and animage calculation device. The image processing device further comprises:an image capturing module, a feature extracting module and aninformation alignment module. The image capturing module is used forobtaining a plurality of images of a body at different time points,while allowing the plural images to include a first image captured at afirst time point and a second image captured at a second time point. Thefeature extracting module is used for obtaining a first feature pointgroup by detecting feature points in the first image, while obtaining asecond feature point group by detecting feature points in the secondimage. The image alignment module is coupled to the feature extractingmodule and is used for aligning the second image with the first imageaccording to the first feature point group and the second feature pointgroup so as to generate an overlapping image information, whereas theoverlapping image information includes a first matching imagecorresponding to the first image and a second matching imagecorresponding to the second image. The image calculation device, that iscoupled to the image processing device, further comprises: a differencecomparison module, provided for sequentially extracting correspondingwindow areas from the first matching image and the second matching imagein the overlapping image information respectively by the use of asliding window mask, while calculating an image difference ratio foreach of the window areas according to the ratio between the number ofmatching points and the number of unmatched points in the correspondingwindow areas of the first and the second matching images.

From the above description, the medical comparison method and system ofthe present disclosure are capable of rapidly comparing medical imagesof a specific area in a patient that are taken at different timeaccording to the image difference ratio so as to locate a region ofvariation from the medical images, by that physicians not only can berelieved from the time-consuming and labor-intensive task of having tomanually search and find all the differences between retinal images thatare taken at different time, but also from possible misdiagnosis causedby human error. Moreover, physicians are enabled to make a moreobjective valuation to determine whether or not the region of variationis an indication of deterioration for assisting the physicians toarrange corresponding tracking procedures.

Further scope of applicability of the present application will becomemore apparent from the detailed description given hereinafter. However,it should be understood that the detailed description and specificexamples, while indicating exemplary embodiments of the disclosure, aregiven by way of illustration only, since various changes andmodifications within the spirit and scope of the disclosure will becomeapparent to those skilled in the art from this detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will become more fully understood from thedetailed description given herein below and the accompanying drawingswhich are given by way of illustration only, and thus are not limitativeof the present disclosure and wherein:

FIG. 1 is a schematic diagram showing a medical image comparison systemaccording to an embodiment of the present disclosure.

FIG. 2 is a flow chart depicting steps performed in a medical imagecomparison method according to an embodiment of the present disclosure.

FIG. 3A is a schematic diagram showing a retinal image of an eye that iscaptured at a first time point according to an embodiment of the presentdisclosure.

FIG. 3B is a schematic diagram showing another retinal image of the sameeye of FIG. 3A that is captured at a second time point.

FIG. 4A is a schematic diagram showing feature points detected from thefirst image of FIG. 3A.

FIG. 4B is a schematic diagram showing feature points detected from thesecond image of FIG. 3B.

FIG. 5A is a schematic diagram showing the aligning of the second imageto the first image according to an embodiment of the present disclosure.

FIG. 5B is a schematic diagram showing an overlapping image informationextracted FIG. 5A.

FIG. 6 is a schematic diagram showing a sliding window mask according toan embodiment of the present disclosure.

FIG. 7A is a schematic of statistic distribution between the imagedifference ratio and cumulative amount of window area according to anembodiment of the present disclosure.

FIG. 7B is a schematic of statistic distribution between the imagedifference ratio and cumulative amount of window area according toanother embodiment of the present disclosure.

FIG. 8A is a schematic of statistic distribution using a connectedcomponent labeling method.

FIG. 8B is a schematic diagram showing an overlapping image informationusing a connected component labeling method according to an embodimentof the present disclosure.

FIG. 8C is a schematic diagram showing an overlapping image informationusing a connected component labeling method according to anotherembodiment of the present disclosure.

FIG. 9A is a schematic of statistic distribution between the imagedifference ratio and cumulative amount in window area according to anembodiment of the present disclosure.

FIG. 9B is a schematic of statistic distribution between the imagedifference ratio and the amount in window area according to anembodiment of the present disclosure.

FIG. 9C is a schematic of statistic distribution of connected regionarea in window area.

DETAILED DESCRIPTION

In the following detailed description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the disclosed embodiments. It will be apparent,however, that one or more embodiments may be practiced without thesespecific details. In other instances, well-known structures and devicesare schematically shown in order to simplify the drawing.

FIG. 1 is a schematic diagram showing a medical image comparison systemaccording to an embodiment of the present disclosure. In FIG. 1, anexemplified medical image comparison system is disclosed, whichcomprises: an image processing device 12, an image calculation device 14and an output device 16.

In this embodiment, the image processing device 12 is used forperforming a series of image processing procedures on images of anobject that are captured at different time points, including importimages, feature extraction, feature points matching, and imagealignment, so as to obtain a correlated image area of the images to beused as a region of interest (ROI) for analysis.

In detail, the image processing device further comprises: an imagecapturing module 122, a feature extracting module 124 and an informationalignment module 126.

In this embodiment, the image capturing module 122 can be any electronicdevice with imaging capability, such as a camcorder with one or moreCCD/CMOS, but it is not limited thereby. In one embodiment, the imagecapturing module 122 can be an image information transceiver interfacethat is used for receiving image information from other imaging unit orfor transmitting image information to other units.

The image capturing module 122 is provided for capturing images of anobject to be detected, which can be a body part of a user, while theimages to be captured can be a map of vein network depicting bloodvessel caliber, crotch angle and vessel angulation. In an embodiment,the image capturing module 122 is an eye exam device to be used forcapturing image of eyes of a body so as to obtain corresponding retinalimages.

In this embodiment, the feature extracting module 124 can be a hardware,e.g. an integrated circuit, a software, e.g. a program, or thecombination of the two. The feature extracting module 124 is coupled tothe image capturing module 122 for receiving images from the imagecapturing module 122 to be used for detecting feature points in thereceived image.

In this embodiment, the image calculation device 14 can be a hardware,e.g. an integrated circuit, a software, e.g. a program, or thecombination of the two. The image calculation device 14 is coupled tothe image processing device 12 for receiving the overlapping imageinformation from the information alignment module 126 of the imageprocessing device 12. The image calculation device 14 is providedenabled to perform a series of image processing procedures on theoverlapping image information for detecting and calculating imagedifference ratios of window areas defined in the overlapping imageinformation while clustering the window areas according to theircorrelation that is determined based upon the image difference ratios,and thus labeling an ROI as a region of variation.

In detail, the image calculation device 14 further comprises: adifference comparison module 142, a cluster connection module 144, andan image labeling module 146.

In this embodiment, the difference comparison module 142 uses a slidingwindow mask to sequentially extract corresponding window areas from twoimages in the overlapping image information and then calculating animage difference ratio for each of the window areas according to theratio between the number of matching points and the number of unmatchedpoints in the corresponding window areas.

In this embodiment, the cluster connection module 144 is coupled to thedifference comparison module 142, which is performed based upon aconnecting component labeling algorithm for clustering the connectivityof the window areas in the overlapping image information according tothe image difference ratio of each of the window areas.

In this embodiment, the image labeling module 146 is coupled to thecluster connection module 144, which is provided for labeling an ROI asa region of variation according to the connectivity of the window areasin the overlapping image information.

In this embodiment, the output device 16 is coupled to the imagecalculation device 14 for outputting the labeled region of variation ofthe overlapping image information, The output device 16 can be a displaypanel that is provided for displaying the labeled region of variationgraphically for assisting the physicians to do medical diagnosis.Nevertheless, the output device 16 is not limited thereby, and in otherembodiments, the labeled region of variation can be outputted andpresented by sounds, characters, lighting, and so on.

FIG. 2 is a flow chart depicting steps performed in a medical imagecomparison method according to an embodiment of the present disclosure.The medical image comparison method S100 of FIG. 2 is adapted for themedical image comparison system 10 of FIG. 1.

In this embodiment, the medical image comparison method S100 includesthe step S110 to step S160.

At step S110, a plurality of images of a body at different time pointsare obtained, whereas the plural images include a first image capturedat a first time point and a second image captured at a second timepoint.

In an embodiment, the first time point and the second time point are notset to be the same, according to that the first image is an imagecaptured earlier to be used as a reference image, while the second imageis an image captured later in time to be used as a comparison image. Inaddition, the two images of the body that are captured at different timepoints can be any two images of the same body that only have to becaptured earlier and later in time, but without being limited byshooting angle, luminance, contrast, saturation and sharpness. FIG. 3Ais a schematic diagram showing a retinal image of an eye that iscaptured at a first time point according to an embodiment of the presentdisclosure. FIG. 3B is a schematic diagram showing another retinal imageof the same eye of FIG. 3A that is captured at a second time point. Inthis embodiment, the image capturing module 122 is enabled to captureretinal images on the same eye of a body at different time points. Asshown in FIG. 3A, the first image 302 that is captured at a first timepoint includes a first blood vessel 312; and as shown in FIG. 3B, thesecond image 304 that is captured at a second time point includes asecond blood vessel 314. Comparing with the positioning of the firstblood vessel 312 in the first image 302, the position of the secondvessel 314 in the second image 304 is shifted.

At step S120, with reference to FIG. 2, a first feature point group isobtained by detecting feature points in the first image, and a secondfeature point group is obtained by detecting feature points in thesecond image. It is noted that the feature points can be the singularpoints detected in the color space or illumination space of the images.

In this embodiment, the feature extracting module 124 uses image featureextraction techniques, such as the scale invariant feature transform(SIFT) algorithm and the speeded-up robust features (SURF) algorithm, toobtain the first feature point group of the first image and the secondfeature point group of the second image. FIG. 4A is a schematic diagramshowing feature points detected from the first image of FIG. 3A. FIG. 4Bis a schematic diagram showing feature points detected from the secondimage of FIG. 3B. As the embodiment shown in FIG. 4A, the featureextracting module 124 uses the aforesaid image feature extractiontechniques to obtain the feature points P11˜P16 of the first image 302and the feature points P21˜P25 of the second image 301, which aredefined respectively as the first feature point group and the secondfeature point group. In this embodiment, the feature points P11˜P15match to the feature points P21˜P25 in an one-on-one manner, whileleaving the feature point P16 to stand along without matching point tobe found in the second image 304. However, the present disclosure is notlimited by the aforesaid embodiment.

At step S130, an overlapping image information 306 is generated byaligning the second image 304 with the first image 302 according to thefirst feature point group and the second feature point group. FIG. 5A isa schematic diagram showing the aligning of the second image to thefirst image according to an embodiment of the present disclosure. FIG.5B is a schematic diagram showing an overlapping image informationextracted FIG. 5A. In this embodiment, the aligning of the second image304 with the first image 302 that is enabled by the use of theinformation alignment module 126 is enabled by the use of a randomsample consensus (RANSAC) algorithm with affine mapping, as shown inFIG. 5A. After aligning the second image 304 with the first image 302, acorrelated image area 306 of the images is detected and used as a regionof interest (ROI) for generating the overlapping image information 308accordingly, as shown in FIG. 5B, whereas the overlapping imageinformation 308 includes a first matching image 402 that iscorresponding to the first image 302, and a second matching image 404,that is corresponding to the second image, as shown in FIG. 6. FIG. 6 isa schematic diagram showing a sliding window mask according to anembodiment of the present disclosure. For clarification, the imagesshown in FIG. 6 is simply provided for separating the first matchingimage 402 from overlapping with the second matching image 404 in theoverlapping image information 308, whereas in reality, the firstmatching image 402 is overlapped with the second matching image 404.

At step S140, a sliding window mask 50 is used for sequentiallyextracting corresponding window areas from the first matching image 402and the second matching image 404 in the overlapping image information308, while calculating an image difference ratio for each of the windowareas according to the ratio between the number of matching points andthe number of unmatched points in the corresponding window areas of thefirst and the second matching images 402, 404,

In an embodiment, the sliding window mask 50 used in the differencecomparison module 142 is formed in a rectangular shape, but it is notlimited thereby and can be in any shape according to actual requirement.By the sequential extraction of the sliding window mask 50 that areperformed respectively on the first matching image 402 and the secondmatching image 404 for obtaining corresponding window areas r_(xy), thefirst matching image 402 and the second matching image 404 arepartitioned and segmented into a plurality of the window areas r_(xy),whereas x and y represents respectively the horizontal coordinate andthe vertical coordinate in a coordinate system.

In this embodiment, any two neighboring window areas that are defined bytwo continue movements of the sliding window mask 50 are not overlappedwith each other, so that there is no overlapping between window areasr_(xy). However, the present disclosure is not limited thereby, and thusin other embodiments, there are partial overlapping between any twoneighboring window areas that are defined by two continue movements ofthe sliding window mask 50, i.e. there can be overlapping between windowareas r_(xy).

In an embodiment, the image difference ratio f(r_(xy)) is represented bythe following formula:

$\begin{matrix}{{{f\left( r_{xy} \right)} = \frac{n_{i} + n_{j}}{\left( {n_{i} + M_{i}} \right) + \left( {n_{j} + M_{j}} \right)}};} & (1) \\{{{f\left( r_{xy} \right)} = \frac{n_{i} \times n_{j}}{\left( {n_{i} + M_{i}} \right) \times \left( {n_{j} + M_{j}} \right)}};} & (2)\end{matrix}$wherein, 0≤f(r_(xy))≤1

In the formula (1) and formula (2), n_(i) is the number of unmatchedpoints in the i^(th) image; n_(j) is the number of unmatched points inthe j^(th) image; M_(i) is the number of matched points in the i^(th)image; M_(j) is the number of matched points in the j^(th) image; andthe i^(th) image and the j^(th) image are images of the same object thatare captured at different time points. In this embodiment, the firstmatching image 402 is defined to be the i^(th) image, and the secondmatching image 404 is defined to be the j^(th) image. In the presentdisclosure, the image difference ratio f(r_(xy)) is the ratio betweenthe number of matching points and the number of unmatched points, asshown in the formula (1) and formula (2), but it is not limited thereby.

In this embodiment, the sliding window mask 50 is enabled to movesimultaneously in the first and the second matching images 402, 404,while extracting corresponding window areas r_(xy) respectively from thefirst matching image 402 and the second matching image 404 in eachmovement. In FIG. 6, assuming the initial position of the sliding windowmask 50 is the r₁₁ window areas in the first matching image 402 and thesecond matching image 404, first an calculation is enabled to obtain theratio between the number of matching points and the number of unmatchedpoints in the window areas r₁₁. That is, by detecting the first featurepoint group in the first matching image 402 and the second feature pointgroup in the second matching image 404 for matching the first featurepoint group with the second feature point group so as to obtain thenumber of matching points and the number of unmatched points; andthereby, the ratio between the number of matching points and the numberof unmatched points can be obtained and used in the formula (1) and theformula (2) for obtaining an image difference ratio f(r₁₁) of the windowarea r₁₁.

Then, the sliding window mask 50 is moved from the window area r₁₁ tothe neighboring window are r₂₁. Similarly, an image difference ratiof(r₂₁) of the window area r₂₁ can be obtained. In this embodiment, thereis no overlapping between the window area r₁₁ and the window are r₂₁.However, in another embodiment that is not provided in this description,there can be overlap between the window area r₁₁ and the window are r₂₁.

In an embodiment, when formula (1) is used for defining the imagedifference ratio f(r_(xy)), the image difference ratio f(r₁₁) of thewindow area r₁₁ is 0, which indicates that all the feature pointsdetected in the first matching image 402 match entirely to thosedetected in the second matching image 404. Thus, it can conclude thatthe difference between the window area r₁₁ of the first matching image402 and the window area r₁₁ of the second matching image 404 is almostneglectable. Accordingly, by the use of the step S140, the imagedifference ratios f(r_(xy)) for all the window areas r_(xy) defined inthe overlapping image information 308 can be obtained and used fordetermining the variation between the first matching image 402 and thesecond matching image 404 for assisting physicians to do medicaldiagnosis.

At the step S150, a process for clustering the connectivity of thewindow areas in the overlapping image information is enabled accordingto the image difference ratio of each of the window areas.

In this embodiment, a cumulative amount of window areas r_(xy) iscalculated with respect to each image difference ratio f(r_(xy)) of thewindow area r_(xy) in the overlapping image information 308 by the useof the cluster connection module 144.

Operationally, since the image difference ratio f(r_(xy)) for eachwindow area r_(xy) in the overlapping image information 308 can beobtained from the step S140, the process then will define a series ofcumulative cluster intervals to be used for clustering window areasr_(xy) whose corresponding image difference ratios f(r_(xy)) isconfirming to the defining of one cumulative interval in the series ofcumulative cluster intervals as the same group. For instance, as theimage difference ratios f(r_(xy)) are values ranged between 0 and 1 anddefining the series of ten cumulative cluster intervals as following:the first cumulative cluster interval is ranged between 0˜0.1, thesecond cumulative cluster interval is ranged between 0˜0.2, . . . , andthe tenth cumulative cluster interval is ranged between 0˜1, accordinglythe window areas r_(xy) can be clustered and assigned to the clustergroup according to their respective image difference ratios f(r_(xy)),and then the cumulative amount of window areas r_(xy) for each of thecumulative cluster intervals can be calculated and obtained. In thisembodiment, a statistic distribution between the image difference ratiof(r_(xy)) and the cumulative amount of window area can be generated,whereas the statistic distribution can be represented as a histogram, abar chart, a pie chart or a line chart. In other embodiments, thestatistic distribution is represented as a table. As shown in FIG. 7Aand FIG. 7B, the statistic distribution are represented as line charts,whereas the horizontal coordinate represents the image difference ratiof(r_(xy)) and the vertical coordinate represents the cumulative amount nof window area r_(xy). As shown in FIG. 7A, with the increasing of theimage difference ratio f(r_(xy)), the increasing of the cumulativeamount n of window area r_(xy) is slowing down which indicate that theregion of variation is concentrating at window areas with smaller imagedifference ratio f(r_(xy)), and there is little difference between thetwo images. Comparing to that shown in FIG. 7B where with the increasingof the image difference ratio f(r_(xy)), the increasing of thecumulative amount n of window area r_(xy) is rising abruptly whichindicate that the region of variation is concentrating at window areaswith larger image difference ratio f(r_(xy)), and there is a bigdifference between the two images.

FIG. 8A is a schematic of statistic distribution using a connectedcomponent labeling method. FIG. 8B is a schematic diagram showing anoverlapping image information using a connected component labelingmethod according to an embodiment of the present disclosure. FIG. 8C isa schematic diagram showing an overlapping image information using aconnected component labeling method according to another embodiment ofthe present disclosure. It is noted that the amount of window area inthe overlapping image information 308 of FIG. 8B as well as the amountof window area in the overlapping image information 308 of FIG. 8C areonly used for illustration, and thus the present disclosure is notlimited thereby. In the statistic distribution shown in FIG. 8A, theimage difference ratio f(r_(xy)) are scanned in a manner from small tolarge in value. In another embodiment, the image difference ratiof(r_(xy)) can be scanned in a manner from large to small in value. Forinstance, in an embodiment, a value k₀ is defined as a specific imagedifference ratio f(r_(xy)), and then a scanning is enabled on theoverlapping image information 308 for locating all the window areas withimage difference ratio f(r_(xy)) that are conforming to the specificvalue k₀ so as to label those window areas r_(xy) as the selected windowareas of the specific value. As the overlapping image information 308shown in FIG. 8B, the image difference ratio f(r₂₃) of the window arear₂₃, the image difference ratio f(r₂₄) of the window area r₂₄, the imagedifference ratio f(r₃₁) of the window area r₃₁, and the image differenceratio f(r₃₃) of the window area r₃₃ are conforming to the specific valuek₀, and thus the window area r₂₃, the window area r₂₄, the window arear₃₁, and the window area r₃₃ are defined as the selected window areas ofthe specific value. In another embodiment shown in FIG. 8C, when aspecific value range k˜d of the image difference ratio f(r_(xy)) isdefined and the scanning indicates that the image difference ratiof(r₂₁) of the window area r₂₁, the image difference ratio f(r₅₁) of thewindow area r₅₁, the image difference ratio f(r₆₁) of the window arear₆₁, the image difference ratio f(r₅₂) of the window area r₅₂, the imagedifference ratio f(r₆₂) of the window area r₆₂, the image differenceratio f(r₄₃) of the window area r₄₃, the image difference ratio f(r₄₄)of the window area r₄₄, the image difference ratio f(r₂₅) of the windowarea r₂₅, the image difference ratio f(r₃₅) of the window area r₃₅, theimage difference ratio f(r₃₆) of the window area r₃₆, the imagedifference ratio f(r₅₅) of the window area r₅₅, the image differenceratio f(r₆₅) of the window area r₆₅, the image difference ratio f(r₅₆)of the window area r₅₆, the image difference ratio f(r₆₆) of the windowarea r₆₆ are conforming to the specific value range k˜d, and thus thosewindow areas are defined as the selected window areas of the specificvalue. Thereafter, a detection is enabled on those selected window areasof the specific value for identifying and grouping the neighboringwindow areas in the selected window areas of the specific value intoregions of variation. In this embodiment, a connected component labelingmethod is used in the detection for identifying and grouping theneighboring window areas in the selected window areas of the specificvalue into regions of variation, whereas the connected componentlabeling method can be a 8-neighbor point connected component labelingmethod, or a 4-neighbor point connected component labeling method.

As shown in FIG. 8B, the window area r₂₃, the window area r₂₄, and thewindow area r₃₃ are neighbors, so that they are connected to form oneregion of variation 20. That is, the present disclosure is able toconnect individual smaller window areas according to defining of theimage difference ratio into one larger region of variation. In addition,as each window area is defined by the use of the sliding window mask 50with known area size, the total area of the region of variation can beobtained as the totality of the window areas in the region of variation.That is, as shown in FIG. 8B, the total area of the region of variation20 is the total area of the window area r₂₃, the window area r₂₄, andthe window area r₃₃. In FIG. 8B, the window area r₃₁ itself forms oneindividual region of area 22 as it is not connected to any otherselected window areas of the specific value, and thus it is defined as avariation region of one-unit-area, whereas the variation region 22 ofFIG. 8A is defined as a variation region of three-unit-area as it is thecollection of three window areas. However, in other embodiments, therecan be partial overlapping in the neighboring window areas in eachmovement of the sliding window mask 50, so that the total area of thevariation region should be the totality of the neighboring window areaafter subtracting the overlapping area.

As shown in FIG. 8C, the window area r₂₁, indicating as the region 23,is a stand along area that is not connected to other selected area andthus is assigned as a variation region of one-unit-area; the window arear₄₃ and the window area r₄₄ are connected into a variation region 24 oftwo-unit-area; the window area r₂₅, the window area r₃₅ and the windowarea r₃₆ are connected into a variation region 25 of three-unit-area;the window area r₅₁, the window area r₅₂, the window area r₆₁, and thewindow area r₆₂ are connected into a variation region 26 offour-unit-area; and similarly the window area r₅₅ and the window arear₅₆, the window area r₆₅, and the window area r₆₆ are connected intoanother variation region 26 of four-unit-area.

At step S150, the connectivity of the window areas in the overlappingimage information is clustered according to the image difference ratioof each of the window areas; and then the flow proceeds to step s160, Atstep S160, the region of variation on the overlapping image informationis identified and labeled. In this embodiment, the output device 16 isused for outputting an overlapping image information 308 and providedfor assisting physicians or medical personnel to do diagnosis bylabeling a specific value b to the variation regions.

FIG. 9A is a schematic of statistic distribution between the imagedifference ratio and cumulative amount in window area according to anembodiment of the present disclosure. The statistic distribution shownin FIG. 9A is a histogram, in which the horizontal coordinate representsthe image difference ratio f(r_(xy)) and the vertical coordinaterepresents the cumulative amount n of window area r_(xy). In otherembodiments, the statistic distribution can be represented using atable. Similar to the forgoing description, the process then will definea series of cumulative cluster intervals d to be used for clusteringwindow areas r_(xy) whose corresponding image difference ratiosf(r_(xy)) is confirming to the defining of one cumulative interval inthe series of cumulative cluster intervals as the same group, whilecounting the total amount of the window areas r_(xy) of the same groupas its cumulative amount n. In the embodiment shown in FIG. 9A, the barat the image difference ratios f(r_(xy)) of k represents the windowareas r_(xy) whose image difference ratios f(r_(xy)) is ranged between0˜k and the cumulative amount n of those window areas r_(xy) is 18; thebar at the image difference ratios f(r_(xy)) of k−d represents thewindow areas r_(xy) whose image difference ratios f(r_(xy)) is rangedbetween 0˜k−d and the cumulative amount n of those window areas r_(xy)is 15; the bar at the image difference ratios f(r_(xy)) of k−2drepresents the window areas r_(xy) whose image difference ratiosf(r_(xy)) is ranged between 0˜k−2d and the cumulative amount n of thosewindow areas r_(xy) is 10; the bar at the image difference ratiosf(r_(xy)) of k−3d represents the window areas r_(xy) whose imagedifference ratios f(r_(xy)) is ranged between 0˜k−3d and the cumulativeamount n of those window areas r_(xy) is 5; the bar at the imagedifference ratios f(r_(xy)) of k−4d represents the window areas r_(xy)whose image difference ratios f(r_(xy)) is ranged between 0˜k−4d and thecumulative amount n of those window areas r_(xy) is 3; the bar at theimage difference ratios f(r_(xy)) of k−5d represents the window areasr_(xy) whose image difference ratios f(r_(xy)) is ranged between 0˜k−5dand the cumulative amount n of those window areas r_(xy) is 2; and thebar at the image difference ratios f(r_(xy)) of k−6d represents thewindow areas r_(xy) whose image difference ratios f(r_(xy)) is rangedbetween 0˜k−6d and the cumulative amount n of those window areas r_(xy)is 1.

From the cumulative amount n of those window areas r_(xy) shown in FIG.9A, it is able to known the amount of window areas for each of the imagedifference ratio f(r_(xy)). FIG. 9B is a schematic of statisticdistribution between the image difference ratio and the amount in windowarea according to an embodiment of the present disclosure. The staticdistribution of FIG. 9B is also a histogram, which is similar to thatshown in FIG. 9A but is not limited thereby. Similarly, the statisticdistribution shown in FIG. 9B is a histogram, in which the horizontalcoordinate represents the image difference ratio f(r_(xy)) and thevertical coordinate represents the amount n1 of window area r_(xy). Inthe embodiment shown in FIG. 9B, a cumulative cluster interval d is usedfor clustering the window areas. As shown in FIG. 9B, the bar at theimage difference ratios f(r_(xy)) of k−6d represents the window areasr_(xy) whose image difference ratios f(r_(xy)) is ranged between 0˜k−6dand the amount n1 of those window areas r_(xy) is 1, whereas in FIG. 9A,the bar at the image difference ratios f(r_(xy)) of k−5d represents thewindow areas r_(xy) whose image difference ratios f(r_(x)) is rangedbetween 0˜k−5d and the cumulative amount n of those window areas r_(xy)is 2, therefore, it can conclude that after subtracting the one windowarea with image difference ration of k−6d, the amount n1 of the windowarea with image difference ratios f(r_(xy)) is ranged between k−5d˜k−6dis 1, and therefore, in FIG. 9B, the bar at the. Similarly, it canconclude that the bar at the image difference ratios f(r_(xy)) of k−5drepresents the amount n1 of those window areas r_(xy) with imagedifference ratios f(r_(xy)) of k−5d is 1. Consequently, in FIG. 9B, theamount n1 of those window areas r_(xy) with image difference ratiosf(r_(xy)) of k−4d is 1; the amount n1 of those window areas r_(xy) withimage difference ratios f(r_(xy)) of k−3d is 2; the amount n1 of thosewindow areas r_(xy) with image difference ratios f(r_(xy)) of k−2d is 5;the amount n1 of those window areas r_(xy) with image difference ratiosf(r_(xy)) of k−d is 5; and the amount n1 of those window areas r_(xy)with image difference ratios f(r_(xy)) of k is 3. In other word, theregion with maximum variation is the region with image difference ratiosf(r_(xy)) of k, and the amount n1 of those window areas r_(xy) withimage difference ratios f(r_(xy)) ranged between k−d˜k is 3; while theregion with minimum variation is the region with image difference ratiosf(r_(xy)) of k−6d, and the amount n1 of those window areas r_(xy) withimage difference ratios f(r_(xy)) ranged between 0˜k−6d is 1.

Thereafter, a connected component labeling algorithm is used by thedefining of a specific value b, that is, when a physician input a valueb, he/she is intended to locate b regions of variation that are mostobvious in the overlapping image information. In an embodiment, if thespecific value b is 3, and in FIG. 9B where the amount n1 of thosewindow areas r_(xy) with image difference ratios f(r_(xy)) of k is 3, bythat the required amount of variation regions are located and labeledfor providing the variation regions to the physician to do diagnosis.

In another embodiment, if the specific value b is 7, and in FIG. 9Bwhere the amount n1 of those window areas r_(xy) with image differenceratios f(r_(xy)) of k is 3 and the amount n1 of those window areasr_(xy) with image difference ratios f(r_(xy)) of k−d is 5, therefore thethere are 8 window areas to be located that is more than the specificamount of 7. Please refer to FIG. 9C, which is a schematic of statisticdistribution of connected region area in window area. In FIG. 9C, thehorizontal coordinate represents the connected region area, which areone-unit-area, two-unit-area, three-unit-area and for-unit-area fromsmall to large, and the vertical coordinate represents the amount of thecorresponding window areas of different sizes. It is noted that thehistogram shown in FIG. 9C represents the window area with imagedifference ratios f(r_(xy)) of k−d of FIG. 8C. In FIG. 9C using thewindow area with image difference ratios f(r_(xy)) of k−d, the windowarea r₂₁, indicating as the region 23, is a stand along area that is notconnected to other selected area and thus is assigned as a variationregion of one-unit-area, and three is only one variation region ofone-unit-area; similarly, there is one variation area of two-unit-area24, which is a connected region of the window area r₄₃ and the windowarea r₄₄; there is one variation area of three-unit-area 25, which is aconnected region of the window area r₂₅, the window area r₃₅ and thewindow area r₃₆; and there are two variation area of four-unit-area 26,which are respectively a region of; the window area r₅₁, the window arear₅₂, the window area r₆₁, and the window area r₆₂, and a region of thewindow area r₅₅ and the window area r₅₆, the window area r₆₅, and thewindow area r₆₆. In this embodiment, regardless the variation regioncontains only one window area or it is an assembly of multiple windowareas, the identified variation regions are ordered according to theirsize, while the four largest variation regions are selected. That is, inthis embodiment, the two variation area of four-unit-area 26 areselected first, and then the variation area of three-unit-area 25, thevariation area of two-unit-area 24 are selected following.

To sum up, the medical comparison method and system of the presentdisclosure are capable of rapidly comparing medical images of a specificarea in a patient that are taken at different time according to theimage difference ratio so as to locate a region of variation from themedical images, by that physicians not only can be relieved from thetime-consuming and labor-intensive task of having to manually search andfind all the differences between retinal images that are taken atdifferent time, but also from possible misdiagnosis caused by humanerror. Moreover, physicians are enabled to make a more objectivevaluation to determine whether or not the region of variation is anindication of deterioration for assisting the physicians to arrangecorresponding tracking procedures.

By the image difference ratios obtained in the present disclosure, thedegree of matching between two images of the same target area can beevaluated and determined, which can be performed without being limitedby the differences in imaging angle, the use of light source, luminanceand parameter configuration between different retinal imaging processesin each imaging.

In addition, by the image difference ratios obtained in the presentdisclosure for defining the connectivity of the window areas, windowareas can be clustered and connected so as to be used for labelingvariation region according to a specific label value, and thus the labelvariation regions can be provided to a physician to be diagnosis.

With respect to the above description then, it is to be realized thatthe optimum dimensional relationships for the parts of the disclosure,to include variations in size, materials, shape, form, function andmanner of operation, assembly and use, are deemed readily apparent andobvious to one skilled in the art, and all equivalent relationships tothose illustrated in the drawings and described in the specification areintended to be encompassed by the present disclosure.

What is claimed is:
 1. A medical image comparison method, comprising thesteps of: obtaining a plurality of images of a body at different timepoints, while allowing the plural images to include a first imagecaptured at a first time point and a second image captured at a secondtime point; obtaining a first feature point group by detecting featurepoints in the first image, while obtaining a second feature point groupby detecting feature points in the second image; enabling an overlappingimage information to be generated by aligning the second image with thefirst image according to the first feature point group and the secondfeature point group, while allowing the overlapping image information toinclude a first matching image corresponding to the first image and asecond matching image corresponding to the second image; andsequentially extracting corresponding window areas from the firstmatching image and the second matching image in the overlapping imageinformation respectively by the use of a sliding window mask, whilecalculating an image difference ratio for each of the window areasaccording to the ratio between the number of matching points and thenumber of unmatched points in the corresponding window areas of thefirst and the second matching images.
 2. The method of claim 1, furthercomprising the step of: clustering the connectivity of the window areasin the overlapping image information according to the image differenceratio of each of the window areas.
 3. The method of claim 2, wherein theclustering of the connectivity of the window areas in the overlappingimage information further comprises the step of: calculating acumulative amount of window areas according to the corresponding imagedifference ratio.
 4. The method of claim 3, wherein the clustering ofthe connectivity of the window areas in the overlapping imageinformation further comprises the step of: labeling the window areaswith image difference ratios that are conforming to a specific value asa specific window area; and determining whether the labeled window areasare neighboring to one another, while connecting the neighboring labeledwindow areas into a region of variation.
 5. The method of claim 4,further comprising the step of: identifying and labeling the region ofvariation on the overlapping image information.
 6. The method of claim1, wherein the obtaining of the first feature point group of the firstimage and the second feature point group of the second image furthercomprises the step of: using a scale invariant feature transform (SIFT)algorithm and a speeded-up robust features (SURF) algorithm to obtainthe first feature point group of the first image and the second featurepoint group of the second image.
 7. The method of claim 1, wherein thealigning of the second image with the first image is enabled by the useof a random sample consensus (RANSAC) algorithm with affine mapping. 8.The method of claim 1, wherein the use of the sliding window maskfurther comprises the step of: partitioning and segmenting the first andthe second images into a plurality of the window areas.
 9. The method ofclaim 8, wherein the window areas is arranged in a manner selected fromthe group consisting of: the window areas are arranged for allowingpartial overlapping area against one another, and the window areas arearranged without overlapping.
 10. A medical image comparison system,comprising: an image processing device, further comprising: an imagecapturing module, for obtaining a plurality of images of a body atdifferent time points, while allowing the plural images to include afirst image captured at a first time point and a second image capturedat a second time point; a feature extracting module, coupling to theimage capturing module, for obtaining a first feature point group bydetecting feature points in the first image, while obtaining a secondfeature point group by detecting feature points in the second image; andan information alignment module, coupling to the feature extractingmodule, for aligning the second image with the first image according tothe first feature point group and the second feature point group so asto generate an overlapping image information, while enabling theoverlapping image information to include a first matching imagecorresponding to the first image and a second matching imagecorresponding to the second image; and an image calculation device,coupling to the image processing device, further comprises: a differencecomparison module, provided for sequentially extracting correspondingwindow areas from the first matching image and the second matching imagein the overlapping image information respectively by the use of asliding window mask, while calculating an image difference ratio foreach of the window areas according to the ratio between the number ofmatching points and the number of unmatched points in the correspondingwindow areas of the first and the second matching images.
 11. The systemof claim 10, further comprising: a cluster connection module, couplingto the difference comparison module, for clustering the connectivity ofthe window areas in the overlapping image information according to theimage difference ratio of each of the window areas.
 12. The system ofclaim 11, further comprising: an image labeling module, coupling to thecluster connection module, for labeling a region of variation on theoverlapping image information.
 13. The system of claim 12, furthercomprising: an output device, coupling to the image labeling module, foroutputting the labeled region of variation of the overlapping imageinformation.
 14. The system of claim 10, wherein the feature extractingmodule uses a scale invariant feature transform (SIFT) algorithm and aspeeded-up robust features (SURF) algorithm to obtain the first featurepoint group of the first image and the second feature point group of thesecond image.
 15. The system of claim 10, wherein the informationalignment module uses a random sample consensus (RANSAC) algorithm withaffine mapping to align the second image with the first image.